Integration of Neural Network and Linear Control Theory Applied to Four-Wheel-Steering Vehicles

In order to improve handling and stability of automobiles, four-wheel-steering (4WS) systems have been studied and developed with remarkable success. Most of the control methods require a linearized two-wheel model of the actual vehicle system which is however strongly influenced by tire nonlinearity especially in critical situations such as emergency maneuvering or collision avoidance on slippery road surface. We propose a new method of designing the four-wheel-steering system, taking into account the nonlinear characteristics of tires and suspensions. For this purpose a new method using an artificial neural network and linear control theory is analyzed and applied to the identification and control of a nonlinear vehicle model structured using software for multibody dynamics (ADAMS). This model takes into account the nonlinear characteristics of actual vehicles with tires which are modeled by the Magic Formula. The results of computer simulation show that the proposed method using neural networks can be efficiently applied to improve the performance of the vehicle.